Finally, a simple method to convert all our bounds for traditional deep learning to counterparts for Bayesian deep learning is given. Our method can be applied to convert other similar bounds for i.i.d. datasets in the research literature as well. Bayesian deep learning has been recently introduced by Wilson and Izmailov [1]}. The key distinguishable property of a Bayesian approach is marginalization, rather than using a single setting of weights in (traditional) deep learning. Bayesian marginalization can particularly improve the accuracy and calibration of modern deep neural networks, which are typically underspecified by the data, and can represent many compelling but different solutions.
